Overview

Brought to you by YData

Dataset statistics

Number of variables22
Number of observations6901265
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory1.0 GiB
Average record size in memory163.0 B

Variable types

Categorical11
Numeric11

Alerts

Severity is highly imbalanced (56.2%) Imbalance
Amenity is highly imbalanced (90.3%) Imbalance
Give_Way is highly imbalanced (95.8%) Imbalance
Junction is highly imbalanced (62.4%) Imbalance
No_Exit is highly imbalanced (97.4%) Imbalance
Railway is highly imbalanced (92.9%) Imbalance
Stop is highly imbalanced (81.7%) Imbalance
Traffic_Calming is highly imbalanced (98.9%) Imbalance
Duration_Seconds is highly skewed (γ1 = 56.22205817) Skewed
Distance(mi) has 2931021 (42.5%) zeros Zeros
Wind_Direction has 915192 (13.3%) zeros Zeros
Wind_Speed(mph) has 915200 (13.3%) zeros Zeros

Reproduction

Analysis started2024-11-05 16:35:03.420775
Analysis finished2024-11-05 16:40:17.795719
Duration5 minutes and 14.37 seconds
Software versionydata-profiling vv4.12.0
Download configurationconfig.json

Variables

Severity
Categorical

Imbalance 

Distinct4
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size105.3 MiB
2
5538813 
3
1128444 
4
 
172262
1
 
61746

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters6901265
Distinct characters4
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row2
2nd row3
3rd row2
4th row3
5th row2

Common Values

ValueCountFrequency (%)
2 5538813
80.3%
3 1128444
 
16.4%
4 172262
 
2.5%
1 61746
 
0.9%

Length

2024-11-05T17:40:17.952293image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-05T17:40:18.019243image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
2 5538813
80.3%
3 1128444
 
16.4%
4 172262
 
2.5%
1 61746
 
0.9%

Most occurring characters

ValueCountFrequency (%)
2 5538813
80.3%
3 1128444
 
16.4%
4 172262
 
2.5%
1 61746
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6901265
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
2 5538813
80.3%
3 1128444
 
16.4%
4 172262
 
2.5%
1 61746
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6901265
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
2 5538813
80.3%
3 1128444
 
16.4%
4 172262
 
2.5%
1 61746
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6901265
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
2 5538813
80.3%
3 1128444
 
16.4%
4 172262
 
2.5%
1 61746
 
0.9%

Distance(mi)
Real number (ℝ)

Zeros 

Distinct21687
Distinct (%)0.3%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean0.5644842
Minimum0
Maximum441.75
Zeros2931021
Zeros (%)42.5%
Negative0
Negative (%)0.0%
Memory size105.3 MiB
2024-11-05T17:40:18.090128image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q10
median0.034
Q30.47
95-th percentile2.679
Maximum441.75
Range441.75
Interquartile range (IQR)0.47

Descriptive statistics

Standard deviation1.7655502
Coefficient of variation (CV)3.1277229
Kurtosis1602.9111
Mean0.5644842
Median Absolute Deviation (MAD)0.034
Skewness19.967062
Sum3895655
Variance3.1171674
MonotonicityNot monotonic
2024-11-05T17:40:18.171474image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 2931021
42.5%
0.01 211640
 
3.1%
0.008 13340
 
0.2%
0.009 12678
 
0.2%
0.007 11258
 
0.2%
0.009999999776 11065
 
0.2%
0.011 10572
 
0.2%
0.03 10343
 
0.1%
0.024 9985
 
0.1%
0.029 9896
 
0.1%
Other values (21677) 3669467
53.2%
ValueCountFrequency (%)
0 2931021
42.5%
0.001 4317
 
0.1%
0.002 2459
 
< 0.1%
0.003 3673
 
0.1%
0.004 5607
 
0.1%
0.005 7429
 
0.1%
0.006 9167
 
0.1%
0.007 11258
 
0.2%
0.008 13340
 
0.2%
0.009 12678
 
0.2%
ValueCountFrequency (%)
441.75 1
< 0.1%
336.5700073 1
< 0.1%
254.3999939 1
< 0.1%
251.2200012 1
< 0.1%
242.3399963 1
< 0.1%
224.5899963 1
< 0.1%
210.0800018 1
< 0.1%
194.7299957 1
< 0.1%
193.4799957 1
< 0.1%
183.1199951 1
< 0.1%

Street
Real number (ℝ)

Distinct320207
Distinct (%)4.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean192814.84
Minimum0
Maximum320206
Zeros4
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size79.0 MiB
2024-11-05T17:40:18.236927image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile37707
Q1156268
median215804
Q3232429
95-th percentile302106
Maximum320206
Range320206
Interquartile range (IQR)76161

Descriptive statistics

Standard deviation75394.482
Coefficient of variation (CV)0.39102012
Kurtosis-0.095058587
Mean192814.84
Median Absolute Deviation (MAD)44716
Skewness-0.65926999
Sum1.3306663 × 1012
Variance5.6843279 × 109
MonotonicityNot monotonic
2024-11-05T17:40:18.328663image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
216455 69302
 
1.0%
216456 66418
 
1.0%
216115 63157
 
0.9%
215646 48920
 
0.7%
215652 47344
 
0.7%
216116 47322
 
0.7%
216351 34938
 
0.5%
216348 31850
 
0.5%
216006 29142
 
0.4%
216302 24827
 
0.4%
Other values (320197) 6438045
93.3%
ValueCountFrequency (%)
0 4
 
< 0.1%
1 1
 
< 0.1%
2 1
 
< 0.1%
3 29
< 0.1%
4 3
 
< 0.1%
5 2
 
< 0.1%
6 1
 
< 0.1%
7 2
 
< 0.1%
8 3
 
< 0.1%
9 13
< 0.1%
ValueCountFrequency (%)
320206 4
 
< 0.1%
320205 1
 
< 0.1%
320204 1
 
< 0.1%
320203 1
 
< 0.1%
320202 1
 
< 0.1%
320201 1
 
< 0.1%
320200 1
 
< 0.1%
320199 2
 
< 0.1%
320198 3
 
< 0.1%
320197 34
< 0.1%

Zipcode
Real number (ℝ)

Distinct774620
Distinct (%)11.2%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean422964.18
Minimum0
Maximum774619
Zeros10
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size79.0 MiB
2024-11-05T17:40:18.402334image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile34069
Q1237922
median450746
Q3632266
95-th percentile737694
Maximum774619
Range774619
Interquartile range (IQR)394344

Descriptive statistics

Standard deviation225476.52
Coefficient of variation (CV)0.53308655
Kurtosis-1.1517707
Mean422964.18
Median Absolute Deviation (MAD)188482
Skewness-0.26569889
Sum2.9189879 × 1012
Variance5.0839659 × 1010
MonotonicityNot monotonic
2024-11-05T17:40:18.487629image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
654009 10471
 
0.2%
650715 8571
 
0.1%
666731 8001
 
0.1%
665596 7887
 
0.1%
337300 7750
 
0.1%
316946 7186
 
0.1%
333271 6515
 
0.1%
547310 6493
 
0.1%
663221 6204
 
0.1%
645483 6156
 
0.1%
Other values (774610) 6826031
98.9%
ValueCountFrequency (%)
0 10
< 0.1%
1 1
 
< 0.1%
2 1
 
< 0.1%
3 1
 
< 0.1%
4 1
 
< 0.1%
5 1
 
< 0.1%
6 8
< 0.1%
7 1
 
< 0.1%
8 1
 
< 0.1%
9 1
 
< 0.1%
ValueCountFrequency (%)
774619 1
 
< 0.1%
774618 7
< 0.1%
774617 3
< 0.1%
774616 1
 
< 0.1%
774615 1
 
< 0.1%
774614 2
 
< 0.1%
774613 7
< 0.1%
774612 2
 
< 0.1%
774611 2
 
< 0.1%
774610 2
 
< 0.1%

Temperature(F)
Real number (ℝ)

Distinct829
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean61.960111
Minimum-45
Maximum196
Zeros2574
Zeros (%)< 0.1%
Negative17638
Negative (%)0.3%
Memory size105.3 MiB
2024-11-05T17:40:18.569156image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum-45
5-th percentile28
Q150
median64
Q376
95-th percentile89
Maximum196
Range241
Interquartile range (IQR)26

Descriptive statistics

Standard deviation19.039681
Coefficient of variation (CV)0.30728933
Kurtosis-0.0065594547
Mean61.960111
Median Absolute Deviation (MAD)13
Skewness-0.52706124
Sum4.2760315 × 108
Variance362.50946
MonotonicityNot monotonic
2024-11-05T17:40:18.647559image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
73 159649
 
2.3%
77 158016
 
2.3%
72 149860
 
2.2%
68 148989
 
2.2%
75 148139
 
2.1%
70 144354
 
2.1%
79 138701
 
2.0%
63 138398
 
2.0%
64 136710
 
2.0%
66 133943
 
1.9%
Other values (819) 5444506
78.9%
ValueCountFrequency (%)
-45 1
 
< 0.1%
-38 3
 
< 0.1%
-36 2
 
< 0.1%
-35 9
< 0.1%
-33 1
 
< 0.1%
-30 1
 
< 0.1%
-29 8
< 0.1%
-28 5
< 0.1%
-27.9 12
< 0.1%
-27.4 3
 
< 0.1%
ValueCountFrequency (%)
196 5
< 0.1%
189 1
 
< 0.1%
174 2
 
< 0.1%
172 2
 
< 0.1%
170.6 1
 
< 0.1%
168.8 1
 
< 0.1%
167 1
 
< 0.1%
162 2
 
< 0.1%
161.6 1
 
< 0.1%
143.6 1
 
< 0.1%

Humidity(%)
Real number (ℝ)

Distinct100
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean64.396618
Minimum1
Maximum100
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size105.3 MiB
2024-11-05T17:40:18.718868image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile24
Q148
median66
Q384
95-th percentile96
Maximum100
Range99
Interquartile range (IQR)36

Descriptive statistics

Standard deviation22.742039
Coefficient of variation (CV)0.35315581
Kurtosis-0.72765416
Mean64.396618
Median Absolute Deviation (MAD)18
Skewness-0.37822121
Sum4.4441812 × 108
Variance517.20036
MonotonicityNot monotonic
2024-11-05T17:40:18.790609image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
93 258266
 
3.7%
100 240251
 
3.5%
87 153111
 
2.2%
90 150704
 
2.2%
89 125416
 
1.8%
96 117802
 
1.7%
81 114953
 
1.7%
84 114935
 
1.7%
82 112275
 
1.6%
86 109189
 
1.6%
Other values (90) 5404363
78.3%
ValueCountFrequency (%)
1 44
 
< 0.1%
2 187
 
< 0.1%
3 641
 
< 0.1%
4 2023
 
< 0.1%
5 3883
 
0.1%
6 5641
0.1%
7 7485
0.1%
8 8894
0.1%
9 10291
0.1%
10 12508
0.2%
ValueCountFrequency (%)
100 240251
3.5%
99 12525
 
0.2%
98 5980
 
0.1%
97 77544
 
1.1%
96 117802
1.7%
95 8330
 
0.1%
94 102755
 
1.5%
93 258266
3.7%
92 59033
 
0.9%
91 33914
 
0.5%

Pressure(in)
Real number (ℝ)

Distinct1126
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean29.519031
Minimum0
Maximum58.63
Zeros2
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size105.3 MiB
2024-11-05T17:40:18.868910image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile27.91
Q129.34
median29.84
Q330.02
95-th percentile30.24
Maximum58.63
Range58.63
Interquartile range (IQR)0.68

Descriptive statistics

Standard deviation1.0090751
Coefficient of variation (CV)0.034183882
Kurtosis20.232115
Mean29.519031
Median Absolute Deviation (MAD)0.25
Skewness-3.5722688
Sum2.0371866 × 108
Variance1.0182325
MonotonicityNot monotonic
2024-11-05T17:40:18.935191image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
29.96 109975
 
1.6%
29.99 107870
 
1.6%
29.94 106728
 
1.5%
30.01 104743
 
1.5%
29.97 100788
 
1.5%
29.91 99909
 
1.4%
30.04 99074
 
1.4%
29.95 98835
 
1.4%
30.03 97640
 
1.4%
30 97444
 
1.4%
Other values (1116) 5878259
85.2%
ValueCountFrequency (%)
0 2
 
< 0.1%
0.02 1
 
< 0.1%
0.29 2
 
< 0.1%
0.39 1
 
< 0.1%
2.99 5
< 0.1%
3 2
 
< 0.1%
3.01 1
 
< 0.1%
3.04 4
< 0.1%
9.9 2
 
< 0.1%
16.71 1
 
< 0.1%
ValueCountFrequency (%)
58.63 7
< 0.1%
58.39 2
 
< 0.1%
58.32 1
 
< 0.1%
58.13 1
 
< 0.1%
58.1 4
< 0.1%
58.04 3
< 0.1%
57.74 1
 
< 0.1%
57.54 2
 
< 0.1%
56.54 1
 
< 0.1%
56.31 1
 
< 0.1%

Visibility(mi)
Real number (ℝ)

Distinct81
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean9.1090536
Minimum0
Maximum140
Zeros6994
Zeros (%)0.1%
Negative0
Negative (%)0.0%
Memory size105.3 MiB
2024-11-05T17:40:19.136578image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2.5
Q110
median10
Q310
95-th percentile10
Maximum140
Range140
Interquartile range (IQR)0

Descriptive statistics

Standard deviation2.6414816
Coefficient of variation (CV)0.2899842
Kurtosis80.609317
Mean9.1090536
Median Absolute Deviation (MAD)0
Skewness2.1313248
Sum62863992
Variance6.9774251
MonotonicityNot monotonic
2024-11-05T17:40:19.202107image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
10 5580997
80.9%
7 190662
 
2.8%
9 168643
 
2.4%
8 133938
 
1.9%
5 127469
 
1.8%
6 113352
 
1.6%
2 112662
 
1.6%
4 107172
 
1.6%
3 106175
 
1.5%
1 95411
 
1.4%
Other values (71) 164784
 
2.4%
ValueCountFrequency (%)
0 6994
 
0.1%
0.06 311
 
< 0.1%
0.1 865
 
< 0.1%
0.12 1704
 
< 0.1%
0.19 40
 
< 0.1%
0.2 7134
 
0.1%
0.25 25633
0.4%
0.31 4
 
< 0.1%
0.38 316
 
< 0.1%
0.4 51
 
< 0.1%
ValueCountFrequency (%)
140 1
 
< 0.1%
111 3
 
< 0.1%
105 1
 
< 0.1%
101 1
 
< 0.1%
100 46
 
< 0.1%
98 1
 
< 0.1%
90 12
 
< 0.1%
80 288
< 0.1%
78 1
 
< 0.1%
76 3
 
< 0.1%

Wind_Direction
Real number (ℝ)

Zeros 

Distinct23
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean10.195958
Minimum0
Maximum22
Zeros915192
Zeros (%)13.3%
Negative0
Negative (%)0.0%
Memory size59.2 MiB
2024-11-05T17:40:19.277192image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14
median11
Q316
95-th percentile21
Maximum22
Range22
Interquartile range (IQR)12

Descriptive statistics

Standard deviation6.9784558
Coefficient of variation (CV)0.68443353
Kurtosis-1.2342516
Mean10.195958
Median Absolute Deviation (MAD)6
Skewness0.0069757308
Sum70365011
Variance48.698845
MonotonicityNot monotonic
2024-11-05T17:40:19.335674image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=23)
ValueCountFrequency (%)
0 915192
 
13.3%
11 404009
 
5.9%
14 372557
 
5.4%
19 368759
 
5.3%
20 365224
 
5.3%
9 356045
 
5.2%
15 353083
 
5.1%
21 342045
 
5.0%
13 337450
 
4.9%
8 320915
 
4.7%
Other values (13) 2765986
40.1%
ValueCountFrequency (%)
0 915192
13.3%
1 267423
 
3.9%
2 249555
 
3.6%
3 259638
 
3.8%
4 101879
 
1.5%
5 293892
 
4.3%
6 249348
 
3.6%
7 246398
 
3.6%
8 320915
 
4.7%
9 356045
 
5.2%
ValueCountFrequency (%)
22 161195
2.3%
21 342045
5.0%
20 365224
5.3%
19 368759
5.3%
18 111360
 
1.6%
17 241755
3.5%
16 174368
2.5%
15 353083
5.1%
14 372557
5.4%
13 337450
4.9%

Wind_Speed(mph)
Real number (ℝ)

Zeros 

Distinct179
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean7.6988973
Minimum0
Maximum1087
Zeros915200
Zeros (%)13.3%
Negative0
Negative (%)0.0%
Memory size105.3 MiB
2024-11-05T17:40:19.402053image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q14.6
median7
Q310.4
95-th percentile17
Maximum1087
Range1087
Interquartile range (IQR)5.8

Descriptive statistics

Standard deviation5.4065039
Coefficient of variation (CV)0.70224394
Kurtosis1117.1177
Mean7.6988973
Median Absolute Deviation (MAD)3
Skewness8.1183667
Sum53132130
Variance29.230285
MonotonicityNot monotonic
2024-11-05T17:40:19.477392image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
0 915200
 
13.3%
5 512320
 
7.4%
6 496040
 
7.2%
3 492665
 
7.1%
7 461892
 
6.7%
8 415707
 
6.0%
9 374028
 
5.4%
10 311990
 
4.5%
12 269587
 
3.9%
4.6 213643
 
3.1%
Other values (169) 2438193
35.3%
ValueCountFrequency (%)
0 915200
13.3%
1 163
 
< 0.1%
1.2 436
 
< 0.1%
2 417
 
< 0.1%
2.3 882
 
< 0.1%
3 492665
7.1%
3.5 199934
 
2.9%
4.6 213643
 
3.1%
5 512320
7.4%
5.8 211891
 
3.1%
ValueCountFrequency (%)
1087 1
 
< 0.1%
984 1
 
< 0.1%
822.8 7
< 0.1%
812 1
 
< 0.1%
703.1 2
 
< 0.1%
580 2
 
< 0.1%
471.8 1
 
< 0.1%
328 1
 
< 0.1%
255 1
 
< 0.1%
254.3 2
 
< 0.1%

Weather_Condition
Real number (ℝ)

Distinct140
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean41.632215
Minimum0
Maximum139
Zeros186
Zeros (%)< 0.1%
Negative0
Negative (%)0.0%
Memory size65.8 MiB
2024-11-05T17:40:19.537270image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile6
Q115
median15
Q383
95-th percentile93
Maximum139
Range139
Interquartile range (IQR)68

Descriptive statistics

Standard deviation36.291427
Coefficient of variation (CV)0.87171502
Kurtosis-1.4401244
Mean41.632215
Median Absolute Deviation (MAD)9
Skewness0.51391306
Sum2.8731495 × 108
Variance1317.0677
MonotonicityNot monotonic
2024-11-05T17:40:19.618590image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
15 2436127
35.3%
83 948760
 
13.7%
7 777517
 
11.3%
89 650552
 
9.4%
6 610683
 
8.8%
60 329166
 
4.8%
86 319799
 
4.6%
103 175020
 
2.5%
68 120388
 
1.7%
17 88460
 
1.3%
Other values (130) 444793
 
6.4%
ValueCountFrequency (%)
0 186
 
< 0.1%
1 259
 
< 0.1%
2 2
 
< 0.1%
3 710
 
< 0.1%
4 899
 
< 0.1%
5 1
 
< 0.1%
6 610683
8.8%
7 777517
11.3%
8 16022
 
0.2%
9 1
 
< 0.1%
ValueCountFrequency (%)
139 322
 
< 0.1%
138 11140
0.2%
137 22
 
< 0.1%
136 178
 
< 0.1%
135 16
 
< 0.1%
134 3
 
< 0.1%
133 2035
 
< 0.1%
132 4027
 
0.1%
131 16895
0.2%
130 2
 
< 0.1%

Amenity
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size105.3 MiB
0
6815353 
1
 
85912

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters6901265
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 6815353
98.8%
1 85912
 
1.2%

Length

2024-11-05T17:40:19.689485image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-05T17:40:19.735286image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 6815353
98.8%
1 85912
 
1.2%

Most occurring characters

ValueCountFrequency (%)
0 6815353
98.8%
1 85912
 
1.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6901265
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 6815353
98.8%
1 85912
 
1.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6901265
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 6815353
98.8%
1 85912
 
1.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6901265
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 6815353
98.8%
1 85912
 
1.2%

Crossing
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size105.3 MiB
0
6109696 
1
791569 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters6901265
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 6109696
88.5%
1 791569
 
11.5%

Length

2024-11-05T17:40:19.787350image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-05T17:40:19.835174image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 6109696
88.5%
1 791569
 
11.5%

Most occurring characters

ValueCountFrequency (%)
0 6109696
88.5%
1 791569
 
11.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6901265
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 6109696
88.5%
1 791569
 
11.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6901265
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 6109696
88.5%
1 791569
 
11.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6901265
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 6109696
88.5%
1 791569
 
11.5%

Give_Way
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size105.3 MiB
0
6869396 
1
 
31869

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters6901265
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 6869396
99.5%
1 31869
 
0.5%

Length

2024-11-05T17:40:19.888928image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-05T17:40:19.935526image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 6869396
99.5%
1 31869
 
0.5%

Most occurring characters

ValueCountFrequency (%)
0 6869396
99.5%
1 31869
 
0.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6901265
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 6869396
99.5%
1 31869
 
0.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6901265
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 6869396
99.5%
1 31869
 
0.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6901265
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 6869396
99.5%
1 31869
 
0.5%

Junction
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size105.3 MiB
0
6399773 
1
 
501492

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters6901265
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 6399773
92.7%
1 501492
 
7.3%

Length

2024-11-05T17:40:19.993021image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-05T17:40:20.035401image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 6399773
92.7%
1 501492
 
7.3%

Most occurring characters

ValueCountFrequency (%)
0 6399773
92.7%
1 501492
 
7.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6901265
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 6399773
92.7%
1 501492
 
7.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6901265
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 6399773
92.7%
1 501492
 
7.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6901265
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 6399773
92.7%
1 501492
 
7.3%

No_Exit
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size105.3 MiB
0
6883495 
1
 
17770

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters6901265
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 6883495
99.7%
1 17770
 
0.3%

Length

2024-11-05T17:40:20.097134image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-05T17:40:20.144764image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 6883495
99.7%
1 17770
 
0.3%

Most occurring characters

ValueCountFrequency (%)
0 6883495
99.7%
1 17770
 
0.3%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6901265
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 6883495
99.7%
1 17770
 
0.3%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6901265
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 6883495
99.7%
1 17770
 
0.3%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6901265
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 6883495
99.7%
1 17770
 
0.3%

Railway
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size105.3 MiB
0
6841971 
1
 
59294

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters6901265
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 6841971
99.1%
1 59294
 
0.9%

Length

2024-11-05T17:40:20.202021image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-05T17:40:20.248224image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 6841971
99.1%
1 59294
 
0.9%

Most occurring characters

ValueCountFrequency (%)
0 6841971
99.1%
1 59294
 
0.9%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6901265
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 6841971
99.1%
1 59294
 
0.9%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6901265
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 6841971
99.1%
1 59294
 
0.9%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6901265
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 6841971
99.1%
1 59294
 
0.9%

Stop
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size105.3 MiB
0
6709990 
1
 
191275

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters6901265
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 6709990
97.2%
1 191275
 
2.8%

Length

2024-11-05T17:40:20.302136image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-05T17:40:20.351872image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 6709990
97.2%
1 191275
 
2.8%

Most occurring characters

ValueCountFrequency (%)
0 6709990
97.2%
1 191275
 
2.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6901265
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 6709990
97.2%
1 191275
 
2.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6901265
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 6709990
97.2%
1 191275
 
2.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6901265
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 6709990
97.2%
1 191275
 
2.8%

Traffic_Calming
Categorical

Imbalance 

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size105.3 MiB
0
6894414 
1
 
6851

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters6901265
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row0
3rd row0
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 6894414
99.9%
1 6851
 
0.1%

Length

2024-11-05T17:40:20.401861image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-05T17:40:20.451902image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 6894414
99.9%
1 6851
 
0.1%

Most occurring characters

ValueCountFrequency (%)
0 6894414
99.9%
1 6851
 
0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6901265
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 6894414
99.9%
1 6851
 
0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6901265
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 6894414
99.9%
1 6851
 
0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6901265
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 6894414
99.9%
1 6851
 
0.1%

Traffic_Signal
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size105.3 MiB
0
5881403 
1
1019862 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters6901265
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row1
2nd row0
3rd row1
4th row0
5th row0

Common Values

ValueCountFrequency (%)
0 5881403
85.2%
1 1019862
 
14.8%

Length

2024-11-05T17:40:20.503794image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-05T17:40:20.555101image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
0 5881403
85.2%
1 1019862
 
14.8%

Most occurring characters

ValueCountFrequency (%)
0 5881403
85.2%
1 1019862
 
14.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6901265
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 5881403
85.2%
1 1019862
 
14.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6901265
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 5881403
85.2%
1 1019862
 
14.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6901265
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 5881403
85.2%
1 1019862
 
14.8%

Civil_Twilight
Categorical

Distinct2
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size105.3 MiB
1
5160127 
0
1741138 

Length

Max length1
Median length1
Mean length1
Min length1

Characters and Unicode

Total characters6901265
Distinct characters2
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st row0
2nd row1
3rd row1
4th row1
5th row1

Common Values

ValueCountFrequency (%)
1 5160127
74.8%
0 1741138
 
25.2%

Length

2024-11-05T17:40:20.604453image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-11-05T17:40:20.651944image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
ValueCountFrequency (%)
1 5160127
74.8%
0 1741138
 
25.2%

Most occurring characters

ValueCountFrequency (%)
1 5160127
74.8%
0 1741138
 
25.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 6901265
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 5160127
74.8%
0 1741138
 
25.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 6901265
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 5160127
74.8%
0 1741138
 
25.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 6901265
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 5160127
74.8%
0 1741138
 
25.2%

Duration_Seconds
Real number (ℝ)

Skewed 

Distinct72892
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23931.489
Minimum120
Maximum1.6877634 × 108
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size105.3 MiB
2024-11-05T17:40:20.713963image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Quantile statistics

Minimum120
5-th percentile1719
Q12014
median4500
Q37506
95-th percentile21600
Maximum1.6877634 × 108
Range1.6877622 × 108
Interquartile range (IQR)5492

Descriptive statistics

Standard deviation758788.58
Coefficient of variation (CV)31.706702
Kurtosis4638.6566
Mean23931.489
Median Absolute Deviation (MAD)2704
Skewness56.222058
Sum1.6515755 × 1011
Variance5.7576012 × 1011
MonotonicityNot monotonic
2024-11-05T17:40:20.788786image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
21600 295021
 
4.3%
1800 95582
 
1.4%
2700 58150
 
0.8%
4500 56342
 
0.8%
3600 51550
 
0.7%
14400 49404
 
0.7%
1786 46589
 
0.7%
1785 46544
 
0.7%
1787 45507
 
0.7%
1784 45129
 
0.7%
Other values (72882) 6111447
88.6%
ValueCountFrequency (%)
120 2
 
< 0.1%
150 3
 
< 0.1%
152 1
 
< 0.1%
180 16
< 0.1%
210 6
 
< 0.1%
221 1
 
< 0.1%
229 1
 
< 0.1%
240 12
< 0.1%
270 5
 
< 0.1%
271 1
 
< 0.1%
ValueCountFrequency (%)
168776340 2
< 0.1%
134184345 1
 
< 0.1%
134181332 3
< 0.1%
134179838 3
< 0.1%
134176830 2
< 0.1%
106135755 1
 
< 0.1%
100954757 1
 
< 0.1%
94755540 1
 
< 0.1%
94697995 1
 
< 0.1%
94697990 1
 
< 0.1%

Interactions

2024-11-05T17:39:53.004866image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T17:38:33.026946image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T17:38:41.207758image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T17:38:49.392956image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T17:38:57.509481image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T17:39:05.630017image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T17:39:13.725810image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T17:39:21.608792image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T17:39:29.429757image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T17:39:37.235408image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T17:39:44.940293image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T17:39:53.732007image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T17:38:33.763598image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T17:38:41.925353image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T17:38:50.145356image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T17:38:58.261231image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T17:39:06.360516image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T17:39:14.434004image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T17:39:22.308531image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T17:39:30.126326image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T17:39:37.923505image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T17:39:45.655854image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T17:39:54.440980image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T17:38:34.476207image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T17:38:42.691086image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T17:38:50.862145image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T17:38:58.988456image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T17:39:07.093487image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T17:39:15.142819image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T17:39:23.008405image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T17:39:30.834967image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T17:39:38.623771image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T17:39:46.374311image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T17:39:55.231710image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T17:38:35.174670image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T17:38:43.448778image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T17:38:51.605186image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T17:38:59.711307image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T17:39:07.843727image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T17:39:15.860577image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T17:39:23.723517image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T17:39:31.561483image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T17:39:39.326275image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T17:39:47.123766image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T17:39:55.971592image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T17:38:35.883045image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T17:38:44.199606image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T17:38:52.347579image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T17:39:00.443759image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T17:39:08.560171image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T17:39:16.660721image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T17:39:24.431050image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T17:39:32.274028image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T17:39:40.006681image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T17:39:47.838542image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T17:39:56.750084image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T17:38:36.590789image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T17:38:44.947535image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T17:38:53.109161image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T17:39:01.187098image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T17:39:09.304023image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T17:39:17.342388image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T17:39:25.152524image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T17:39:32.990761image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T17:39:40.706537image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T17:39:48.573100image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T17:39:57.498299image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T17:38:37.293078image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T17:38:45.690129image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T17:38:53.838656image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T17:39:01.926566image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T17:39:10.043107image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T17:39:18.043819image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T17:39:25.829764image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T17:39:33.709731image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T17:39:41.406541image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T17:39:49.305442image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T17:39:58.196362image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T17:38:38.282486image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T17:38:46.433246image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T17:38:54.573646image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T17:39:02.643690image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T17:39:10.774419image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T17:39:18.727297image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T17:39:26.524488image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T17:39:34.390284image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T17:39:42.089746image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T17:39:50.024800image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T17:39:58.954941image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T17:38:38.998602image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T17:38:47.178556image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T17:38:55.306268image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T17:39:03.377207image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T17:39:11.492752image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T17:39:19.444581image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T17:39:27.251392image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T17:39:35.103301image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T17:39:42.773002image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T17:39:50.731395image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T17:39:59.654557image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T17:38:39.679188image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T17:38:47.911663image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T17:38:56.036304image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T17:39:04.096575image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T17:39:12.226733image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T17:39:20.125276image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T17:39:27.941628image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T17:39:35.806964image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T17:39:43.456385image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T17:39:51.422132image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T17:40:00.337674image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T17:38:40.446217image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T17:38:48.661272image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T17:38:56.770578image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T17:39:04.894008image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T17:39:13.009558image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T17:39:20.892869image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T17:39:28.707907image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T17:39:36.534263image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T17:39:44.222926image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
2024-11-05T17:39:52.140850image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/

Correlations

2024-11-05T17:40:20.851849image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
AmenityCivil_TwilightCrossingDistance(mi)Duration_SecondsGive_WayHumidity(%)JunctionNo_ExitPressure(in)RailwaySeverityStopStreetTemperature(F)Traffic_CalmingTraffic_SignalVisibility(mi)Weather_ConditionWind_DirectionWind_Speed(mph)Zipcode
Amenity1.0000.0060.1480.0000.0010.0060.0150.0260.0140.0230.0500.0380.0340.0670.0100.0230.1040.0020.0190.0150.0000.043
Civil_Twilight0.0061.0000.0380.0030.0070.0050.2430.0140.0040.0340.0000.0560.0010.0640.2780.0000.0440.0150.1090.1500.0010.066
Crossing0.1480.0381.0000.0040.0070.0580.0350.0880.0620.0350.1790.1210.1190.1940.0610.0370.4750.0070.0510.0290.0010.146
Distance(mi)0.0000.0030.0041.0000.3910.000-0.0130.0000.000-0.1060.0000.0050.002-0.178-0.0580.0010.004-0.012-0.023-0.051-0.002-0.032
Duration_Seconds0.0010.0070.0070.3911.0000.001-0.0130.0080.000-0.1140.0010.0090.003-0.215-0.0300.0000.0080.005-0.041-0.072-0.0360.017
Give_Way0.0060.0050.0580.0000.0011.0000.0040.0090.0070.0080.0030.0080.0300.0380.0050.0030.0720.0070.0100.0070.0000.045
Humidity(%)0.0150.2430.035-0.013-0.0130.0041.0000.0090.0090.0430.0060.0270.0260.043-0.3310.0050.021-0.4650.100-0.186-0.198-0.121
Junction0.0260.0140.0880.0000.0080.0090.0091.0000.0040.0270.0090.0530.0360.1610.0270.0050.1040.0040.0250.0220.0000.090
No_Exit0.0140.0040.0620.0000.0000.0070.0090.0041.0000.0070.0040.0120.0260.0230.0090.0130.0300.0070.0050.0030.0000.022
Pressure(in)0.0230.0340.035-0.106-0.1140.0080.0430.0270.0071.0000.0160.0420.0030.0660.0180.0050.0340.0780.035-0.0120.001-0.107
Railway0.0500.0000.1790.0000.0010.0030.0060.0090.0040.0161.0000.0140.0070.0280.0100.0050.0590.0030.0090.0050.0000.040
Severity0.0380.0560.1210.0050.0090.0080.0270.0530.0120.0420.0141.0000.0590.1650.0380.0060.1200.0120.0900.0540.0010.081
Stop0.0340.0010.1190.0020.0030.0300.0260.0360.0260.0030.0070.0591.0000.0960.0160.0270.0480.0010.0180.0060.0000.060
Street0.0670.0640.194-0.178-0.2150.0380.0430.1610.0230.0660.0280.1650.0961.000-0.0320.0170.219-0.0250.0380.0550.045-0.058
Temperature(F)0.0100.2780.061-0.058-0.0300.005-0.3310.0270.0090.0180.0100.0380.016-0.0321.0000.0060.0470.2290.0730.1030.0870.011
Traffic_Calming0.0230.0000.0370.0010.0000.0030.0050.0050.0130.0050.0050.0060.0270.0170.0061.0000.0110.0020.0040.0040.0000.014
Traffic_Signal0.1040.0440.4750.0040.0080.0720.0210.1040.0300.0340.0590.1200.0480.2190.0470.0111.0000.0020.0670.0370.0020.150
Visibility(mi)0.0020.0150.007-0.0120.0050.007-0.4650.0040.0070.0780.0030.0120.001-0.0250.2290.0020.0021.000-0.1320.0900.055-0.014
Weather_Condition0.0190.1090.051-0.023-0.0410.0100.1000.0250.0050.0350.0090.0900.0180.0380.0730.0040.067-0.1321.0000.0270.113-0.085
Wind_Direction0.0150.1500.029-0.051-0.0720.007-0.1860.0220.003-0.0120.0050.0540.0060.0550.1030.0040.0370.0900.0271.0000.3470.026
Wind_Speed(mph)0.0000.0010.001-0.002-0.0360.000-0.1980.0000.0000.0010.0000.0010.0000.0450.0870.0000.0020.0550.1130.3471.000-0.049
Zipcode0.0430.0660.146-0.0320.0170.045-0.1210.0900.022-0.1070.0400.0810.060-0.0580.0110.0140.150-0.014-0.0850.026-0.0491.000

Missing values

2024-11-05T17:40:00.537201image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-11-05T17:40:04.406509image/svg+xmlMatplotlib v3.9.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

SeverityDistance(mi)StreetZipcodeTemperature(F)Humidity(%)Pressure(in)Visibility(mi)Wind_DirectionWind_Speed(mph)Weather_ConditionAmenityCrossingGive_WayJunctionNo_ExitRailwayStopTraffic_CalmingTraffic_SignalCivil_TwilightDuration_Seconds
220.0129231642000936.0100.029.6710.0153.58600000000101800.0
330.0121630442612435.196.029.649.0154.68300000000011800.0
420.0123559442796236.089.029.656.0153.58300000000111800.0
530.0131517941003337.997.029.637.0143.56000000000011800.0
620.0024700442631234.0100.029.667.0213.58600000000011800.0
730.0124395442436934.0100.029.667.0213.58600000000011800.0
820.0025307342428633.399.029.675.0151.28300000000011800.0
930.0131517941003337.4100.029.623.0144.66000000000011800.0
1030.0125764941227335.693.029.645.0205.89301010000011800.0
1130.0121627840980237.4100.029.623.0144.66000000000011800.0
SeverityDistance(mi)StreetZipcodeTemperature(F)Humidity(%)Pressure(in)Visibility(mi)Wind_DirectionWind_Speed(mph)Weather_ConditionAmenityCrossingGive_WayJunctionNo_ExitRailwayStopTraffic_CalmingTraffic_SignalCivil_TwilightDuration_Seconds
772838420.39021564665071578.052.029.6910.0176.01500000000011723.0
772838520.00016831166782688.032.028.2010.02010.01500000000011703.0
772838620.18919485467370873.068.029.7610.0199.01500010000011703.0
772838720.44328429967168675.060.029.7410.0149.01500000000011711.0
772838820.00020532764754581.048.028.7810.036.01500000000011711.0
772838920.54326319366615986.040.028.9210.01913.01500000000011716.0
772839020.33821634366001370.073.029.3910.0156.01500000000011613.0
772839120.56120293167274873.064.029.7410.01410.08900010000011708.0
772839220.77228372163836571.081.029.6210.0158.01500000000011761.0
772839320.53716820066424579.047.028.637.0157.01500000000011765.0